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Published at November 27

MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data

cs.AI

cs.SI

Released Date: November 27, 2024

Authors: Miao Fan¹, Jizhou Huang¹, An Zhuo¹, Ying Li¹, Ping Li¹, Haifeng Wang¹

Aff.: ¹Baidu Inc.

Arxiv: http://arxiv.org/abs/2411.18085v1

Refer to caption

Method	Mean Absolute Error (MAE)	Root Mean Squared Error (RMSE)	R² Score (R²)
Avg. Prices (citywide)	$18,631\pm 4,517$ CNY/m²	$25,151\pm 6,260$ CNY/m²	$0.0001\pm 0.0001$
Macro Avg. Prices (within $0.5$ km)	$15,110\pm 3,928$ CNY/m²	$21,593\pm 5,826$ CNY/m²	$0.2668\pm 0.1580$
Macro Avg. Prices (within $1.0$ km)	$14,963\pm 3,771$ CNY/m²	$20,745\pm 5,492$ CNY/m²	$0.3251\pm 0.1349$
Micro Avg. Prices (within $0.5$ km)	$15,089\pm 3,889$ CNY/m²	$21,589\pm 5,793$ CNY/m²	$0.2690\pm 0.1572$
Micro Avg. Prices (within $1.0$ km)	$14,172\pm 3,650$ CNY/m²	$20,575\pm 5,508$ CNY/m²	$0.3361\pm 0.1342$
Linear Regression (within $0.5$ km)	$12,764\pm 3,250$ CNY/m²	$18,897\pm 5,086$ CNY/m²	$0.4400\pm 0.0414$
Linear Regression (within $1.0$ km)	$10,717\pm 2,150$ CNY/m²	$16,303\pm 4,027$ CNY/m²	$0.5769\pm 0.0512$
Boosting Trees (within $0.5$ km)	$11,515\pm 2,847$ CNY/m²	$18,650\pm 4,617$ CNY/m²	$0.5082\pm 0.0681$
Boosting Trees (within $1.0$ km)	$10,594\pm 2,085$ CNY/m²	$16,125\pm 4,018$ CNY/m²	$0.5846\pm 0.0670$
DNN (within $0.5$ km)	$11,280\pm 2,425$ CNY/m²	$17,144\pm 4,359$ CNY/m²	$0.5351\pm 0.0484$
DNN (within $1.0$ km)	$9,947\pm 1,845$ CNY/m²	$15,440\pm 3,580$ CNY/m²	$0.6161\pm 0.0566$
Monopoly (within $0.5$ km)	$9,531\pm 1,745$ CNY/m²	$15,352\pm 3,764$ CNY/m²	$0.6192\pm 0.0746$
Monopoly (within $1.0$ km)	$9,544\pm 1,728$ CNY/m²	$15,234\pm 3,640$ CNY/m²	$0.6231\pm 0.0773$